text: _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
  model = dict(
      pretrained='torchvision://resnext50_32x4d',
      backbone=dict(
          type='ResNeXt',
          depth=50,
          groups=32,
          base_width=4,
          num_stages=4,
          out_indices=(0, 1, 2, 3),
          frozen_stages=1,
          norm_cfg=dict(type='BN', requires_grad=True),
          style='pytorch'),
      roi_head=dict(
          bbox_head=dict(num_classes=1),
          mask_head=dict(num_classes=1)))
  # dataset settings
  dataset_type = 'OneCocoDataset'
  data_root = '/home/baikai/Desktop/AliComp/datasets/PreRoundData/'
  classes = ('person', )
  img_norm_cfg = dict(
      mean=[113.096, 107.866, 107.388], std=[66.571, 65.357, 66.714], to_rgb=True)
  train_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
      dict(type='Resize',
           img_scale=[(640, 320), (640, 352), (640, 384),
                      (640, 416), (640, 448), (640, 480)],
           multiscale_mode='value',
           keep_ratio=True),
      dict(type='RandomFlip', flip_ratio=0.5),
      dict(type='Normalize', **img_norm_cfg),
      dict(type='Pad', size_divisor=32),
      dict(type='DefaultFormatBundle'),
      dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
  ]
  test_pipeline = [
      dict(type='LoadImageFromFile'),
      dict(
          type='MultiScaleFlipAug',
          img_scale=[(640, 352), (640, 416), (640, 480)],
          flip=True,
          transforms=[
              dict(type='Resize', keep_ratio=True),
              dict(type='RandomFlip'),
              dict(type='Normalize', **img_norm_cfg),
              dict(type='Pad', size_divisor=32),
              dict(type='ImageToTensor', keys=['img']),
              dict(type='Collect', keys=['img']),
          ])
  ]
  data = dict(
      samples_per_gpu=2,
      workers_per_gpu=2,
      train=dict(
          type=dataset_type,
          ann_file=data_root + 'coco_annos_train.json',
          img_prefix=data_root + 'JPEGImages/',
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=data_root + 'coco_annos_val.json',
          img_prefix=data_root + 'JPEGImages/',
          pipeline=test_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=data_root + 'coco_annos_val.json',
          img_prefix=data_root + 'JPEGImages/',
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
  # runtime settings
  work_dir = 'train_ws/x50_mstrain_1x'
  